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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS1997

Rosat X-Ray Observations

This grant has supported ongoing analysis of ROSAT X-Ray observations. Publications resulting in whole or in part from this grant include: X-Rays and Activity in Pre-Main Sequence Stars. The Rest of Orion: Low Mass Pre-Main Sequence Stars Outside the Orion Nebula. Star Formation in Orion (the Constellation). Discovery of a New Cataclysmic Variable System. An IDL-based ROSAT Data Analysis Package. The Long Period AM Her-like Cataclysmic Variable RXJ051541+0104.611. Discovery of A Nearby Isolated Neutron Star. A candidate isolated old neutron star. On the X-ray Position and Deep Optical Imaging of the Neutron Star Candidate RXJ1856.5-375411. The Spectral Energy Distribution of the Isolated Neutron Star RXJ185635-3754. A Most Unusual, Highly-X-ray-Variable Cataclysmic Variable.

Walter, F. M.↗

NASA NTRS2002

XTE J1946+274: An Enigmatic X-Ray Pulsar

XTE J1946+274 = GRO J1944+26 is a 15.8-s X-ray pulsar discovered simultaneously by the Rossi X-ray Timing Explorer (RXTE) and the Burst and Transient Source Experiment (BATSE) in September 1998. Follow-up optical/IR observations resulted in the discovery of a Be star companion. Our pulse timing analysis of BATSE and RXTE data indicates that the orbital period is approximately 169 days. Since its discovery in 1998, XTE J1946+274 has undergone 13 outbursts. These outbursts axe not regularly spaced. They occur approximately twice per orbit and are not locked in orbital phase, unlike most Be/X-ray transient systems. A possible explanation for this is a global-one armed oscillation or density perturbation propagating rapidly in the Be star's disk. We will investigate radial velocity variations in the central peak of the H-alpha line to look for evidence of such a perturbation. From 2001 March-September, we regularly monitored XTE J1946+274 with the RXTE PCA. We will demonstrate that the spectrum appears to be varying with orbital phase, based on the 2001 and 1998 RXTE PCA observations. We will also present histories of pulsed frequency and flux.

Wilson, Colleen A.↗

DOE OSTI2026

Uncertainty-Aware Machine Learning for Small-Angle X-ray Scattering Analysis in Autonomous Experimentation

Small-angle X-ray scattering (SAXS) is a powerful high-throughput characterization tool for probing nanoscale structure in native sample environments, providing real-time morphological information such as nanoparticle size and shape during synthesis. However, automated SAXS data analysis for extracting meaningful structural parameters is non-trivial and remains a bottleneck in closed-loop experimentation towards autonomous materials discovery, which demands fast, reliable, and uncertainty-aware data analysis. Here, we develop a machine-learning approach for automated SAXS analysis tailored to closed-loop nanoparticle synthesis. A Random Forest (RF) regression model is trained on 100,000 synthetic SAXS curves generated from polydisperse spherical nanoparticles with realistic background contributions. Using normalized one-dimensional SAXS intensity profiles as input, the RF model directly predicts nanoparticle radius, size polydispersity, and background parameters, while the ensemble standard deviation across trees provides built-in uncertainty quantification (UQ). On synthetic data, we show that combining fit-quality metrics (R 2 , MAE) with thresholds on prediction uncertainty reliably identifies accurate parameter estimates without access to ground truth. We then apply the trained model to 365 experimental SAXS profiles of citrate-reduced gold nanoparticles synthesized using an automated droplet-flow microreactor with in situ SAXS at a synchrotron beamline, classifying the results into high- and low-confidence subsets based on UQ metrics. Finally, we integrate RF-based SAXS analysis into a simulated closed-loop optimization campaign using Gaussian process Bayesian optimization to minimize nanoparticle polydispersity, benchmarking against conventional automated Levenberg–Marquardt fitting. The RF-guided campaign exhibits substantially faster convergence and lower relative opportunity cost (∼0.07 vs ∼0.3), demonstrating that uncertainty-aware machine-learning SAXS analysis significantly enhances the efficiency and robustness of autonomous nanomaterials synthesis workflows.

Bayesian optimization↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth independence and autonomy of mission operations. Here we present an overview of AI/ML architecture to support deep space mission goals, developed with leaders in the field. First, we focus on the fundamental biological research that supports our understanding of physiological responses to spaceflight, and we describe current efforts to support AI/ML research including data standardization and data engineering through maximally open and FAIR (findable, accessible, interoperable, reusable) databases and the generation of AI-ready datasets for reuse and analysis. We also discuss remote data management frameworks for research data as well as environmental and health data that are generated during deep space missions. We highlight several research projects that leverage data standardization and management for fundamental biological discovery to uncover the complex effects of space travel on living systems. Next, we provide an overview of cutting-edge AI/ML approaches that can be integrated to support remote monitoring and analysis during deep space missions, including generative models and large language models to learn the underlying biomedical patterns and predict outcomes or answer questions during off world medical scenarios. We also describe current AI/ML methods to support this research and monitoring through automated cloud-based labs which enable limited human intervention and closed-loop experimentation in remote settings. These labs could support mission autonomy by analyzing environmental data streams, and would be facilitated through in situ analytics capabilities to avoid sending large raw data files through low bandwidth communications. Finally, in the context of deep space missions with limited communications or access to medical advice from Earth, we describe a solution for integrated, real-time mission biomonitoring across hierarchical levels from continuous environmental monitoring, to wearables and point-of-care devices, to molecular and physiological monitoring. We introduce a precision space health system that will ensure that the future of space health is predictive, preventative, participatory and personalized.

artificial intelligence↗

NASA NTRS2010

A History of NASA Remote Sensing Contributions to Archaeology

During its long history of developing and deploying remote sensing instruments, NASA has provided a scientific data that have benefitted a variety of scientific applications among them archaeology. Multispectral and hyperspectral instrument mounted on orbiting and suborbital platforms have provided new and important information for the discovery, delineation and analysis of archaeological sites worldwide. Since the early 1970s, several of the ten NASA centers have collaborated with archaeologists to refine and validate the use of active and passive remote sensing for archeological use. The Stennis Space Center (SSC), located in Mississippi USA has been the NASA leader in archeological research. Together with colleagues from Goddard Space Flight Center (GSFC), Marshall Space Flight Center (MSFC), and the Jet Propulsion Laboratory (JPL), SSC scientists have provided the archaeological community with useful images and sophisticated processing that have pushed the technological frontiers of archaeological research and applications. Successful projects include identifying prehistoric roads in Chaco canyon, identifying sites from the Lewis and Clark Corps of Discovery exploration and assessing prehistoric settlement patterns in southeast Louisiana. The Scientific Data Purchase (SDP) stimulated commercial companies to collect archaeological data. At present, NASA formally solicits "space archaeology" proposals through its Earth Science Directorate and continues to assist archaeologists and cultural resource managers in doing their work more efficiently and effectively. This paper focuses on passive remote sensing and does not consider the significant contributions made by NASA active sensors. Hyperspectral data offers new opportunities for future archeological discoveries.

Giardino, Marco J.↗

DOE OSTI2024

Probabilistic data fusion and physics-informed machine learning: A new paradigm for modeling under uncertainty, and its application to accelerating the discovery of new materials

In this report we summarize the work conducted by PI Perdikaris and his group under this Early Career project DE–SC0019116 during the period of 09/01/2018 – 08/31/2023. The central aim of the work was to introduce a new paradigm for scientific data analysis that can seamlessly synthesize rigorous mathematical modeling with data of variable fidelity (e.g., measurements at multiple scales/resolutions or predictions of variable fidelity models) and multiple modalities (e.g., images, time–series, or scattered measurements). The setting we are interested in involves complex systems that are partially observed and whose dynamical behavior could be hard to model or totally unknown. The inherent uncertainty associated with this setting necessitates a departure from the classical deterministic realm of modeling and scientific computation, and, consequently, our main building blocks can no longer be crisp deterministic numbers and governing laws, but instead we must operate with probabilistic models.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2002

VIZARD: analysis of Affymetrix Arabidopsis GeneChip data

SUMMARY: The Affymetrix GeneChip Arabidopsis genome array has proved to be a very powerful tool for the analysis of gene expression in Arabidopsis thaliana, the most commonly studied plant model organism. VIZARD is a Java program created at the University of California, Berkeley, to facilitate analysis of Arabidopsis GeneChip data. It includes several integrated tools for filtering, sorting, clustering and visualization of gene expression data as well as tools for the discovery of regulatory motifs in upstream sequences. VIZARD also includes annotation and upstream sequence databases for the majority of genes represented on the Affymetrix Arabidopsis GeneChip array. AVAILABILITY: VIZARD is available free of charge for educational, research, and not-for-profit purposes, and can be downloaded at http://www.anm.f2s.com/research/vizard/ CONTACT: moseyko@uclink4.berkeley.edu.

Non-NASA Center↗

NASA NTRS2002

Laboratory Astrophysics Needs of the Herschel Space Observatory

The science teams of the Herschel Space Observatory have identified a number of areas where laboratory study is required for proper interpretation of Herschel observational data. The most critical is the collection and compilation of laboratory data on spectral line frequencies, transition probabilities and energy levels for the known astrophysical atomic and molecular species in 670 to 57 micron wavelength range of Herschel. The second most critical need is the compilation of collisional excitation cross sections for the species known to dominate the energy balance in the ISM and the temperature dependent chemical reaction rates. On the theoretical front, chemical and radiative transfer models need to be prepared in advance to assess calibration and identify instrument anomalies. In the next few years there will be a need to incorporate spectroscopists and theoretical chemists into teams of astronomers so that the spectroscopic surveys planned can he properly calibrated and rapidly interpreted once the data becomes available. The science teams have also noted that the enormous prospects for molecular discovery will be greatly handicapped by the nearly complete lack of spectroscopic data for anything not already well known in the ISM. As a minimum, molecular species predicted to exist by chemical models should be subjected to detailed laboratory study to ensure conclusive detections. This has the greatest impact on any astrobiology program that might be proposed for Herschel. Without a significant amount of laboratory work in the very near future Herschel will not be prepared for many planned observations, much less addressing the open questions in molecular astrophysics.

Pearson, J. C.↗

NASA NTRS2011

An Ontology Driven Information Architecture for Interoperable Disparate Data Sources

The mission of the Planetary Data System is to facilitate achievement of NASA's planetary science goals by efficiently collecting, archiving, and making accessible digital data produced by or relevant to NASA's planetary missions, research programs, and data analysis programs. The vision is: (1) To gather and preserve the data obtained from exploration of the Solar System by the U.S. and other nations (2) To facilitate new and exciting discoveries by providing access to and ensuring usability of those data to the worldwide community (3) To inspire the public through availability and distribution of the body of knowledge reflected in the PDS data collection PDS is a federation of heterogeneous nodes including science and support nodes

planetary data systems↗

DOE OSTI2025

Nearby stellar substructures in the Galactic halo from DESI Milky Way Survey Year 1 Data Release

We report five nearby ($d_{\mathrm{helio}} < 5$ kpc) stellar substructures in the Galactic halo from a subset of 138 661 stars in the Dark Energy Spectroscopic Instrument (DESI) Milky Way Survey Year 1 Data Release. With an unsupervised clustering algorithm, HDBSCAN*, these substructures are independently identified in Integrals of Motion ($E_{\rm tot}$, $L_{\rm z}$, $\log {J_r}$, $\log {J_z}$) space and Galactocentric cylindrical velocity space ($V_{R}$, $V_{\phi }$, $V_{z}$). We associate all identified clusters with known nearby substructures (Helmi streams, M18-Cand10/MMH-1, Sequoia, Antaeus, and ED-2) previously reported in various studies. With metallicities precisely measured by DESI, we confirm that the Helmi streams, M18-Cand10, and ED-2 are chemically distinct from local halo stars. We have characterized the chemodynamic properties of each dynamic group, including their metallicity dispersions, to associate them with their progenitor types (globular cluster or dwarf galaxy). Our approach for searching substructures with HDBSCAN* reliably detects real substructures in the Galactic halo, suggesting that applying the same method can lead to the discovery of new substructures in future DESI data. With more stars from future DESI data releases and improved astrometry from the upcoming Gaia Data Release 4, we will have a more detailed blueprint of the Galactic halo, offering a significant improvement in our understanding of the formation and evolutionary history of the Milky Way Galaxy.

dynamics↗

NASA NTRSDate not supplied

NASA Institutional Scientific Collection and Biospecimen Sharing Program at Ames Research Center

For decades, NASA and their international partners have flown and conducted non-human biological experiments in space to understand the effects of spaceflight and address potential biological hazards. It is imperative to understand the basic science and health risks associated with spaceflight, along with developing countermeasures, as humanity ventures back to the Moon, and then to Mars and beyond. Sending organisms into space is a costly endeavor which makes space-flown biological specimens a valuable resource. To enable maximum scientific return, samples not required by the Principal Investigators are harvested, preserved and archived in NASA’s Institutional Scientific Collection (ISC) at Ames Research Center (ARC). These specimens are collected according to well-established SOPs that maintain their quality and integrity. To enable new discoveries, the samples are then made available to the international scientific community through NASA’s Biospecimen Sharing Program (BSP). The NASA ISC currently stores over 32,000 specimens from Shuttle, International Space Station and ground-based investigations. Tissues are predominantly from mice and rats, though samples are also available from bacteria and quail. The specimens include tissues from all biological systems including musculoskeletal, neurosensory, reproductive, respiratory, circulatory, and digestive and are stored at -80°C, -20°C, +4°C, or ambient. Descriptive metadata is available for all samples. Historically, these tissues have been used for a wide range of analyses, including histology, genomics, and transcriptomics. Also available through the ISC are tissue from NASA’s Space Radiation Laboratory. To study the effects of space radiation, researchers irradiate biological specimens and unused samples are made available through the LSDA. These biospecimens and data are made available through the public Life Sciences Data Archive (LSDA) website to promote basic discovery, pre-clinical and clinical science. Visit the NASA ISC-BSP website for more information. Websites: https://lsda.jsc.nasa.gov/ ; https://www.nasa.gov/ames/research/space-biosciences/isc-bsp

Space Flown Biospecimens↗

NASA NTRSDate not supplied

NASA Institutional Scientific Collection and Biospecimen Sharing Program at Ames Research Center

Space Flown Biospecimens↗

NASA NTRS1999

Ice Flow in the Northeast Greenland Ice Stream

As the use of satellite data for icesheet research has accelerated, new discoveries have changed the way that we think about ice discharge, and new tools have changed the way that we measure it. We have begun to apply a combination of remote sensing and ice sheet modeling techniques to study ice flow in the recently identified northeast Greenland ice stream. This ice stream shows evidence of organized flow far into the interior and has low-slope areas of rapid flow and regions of enhanced shear that resemble the ice streams of West Antarctica. The onset of rapid flow close to the ice divide and the evolution of its flow pattern, however, make this ice stream unique. Our investigation is aimed at increasing our understanding of this ice stream and its origins and role in the discharge of ice from the ice sheet and the effects that discharge may have on the history and evolution of the ice in northern Greenland. The ice flow data comes from satellite radar interferometry tied to reference points. We have applied this technique to measure the velocity over the entire ice stream, which is more than 700 km in length. The velocity contours reveal distinct, well-developed shear margins, which are coincident with linear features in the Synthetic Aperture Radar (SAR) amplitude imagery. It is surprising to see such defined, enhanced flow so far inland and so close to the ice divide. The velocity data and topography data are being used to feed a modeling effort that allows us to study the dynamics and character rapid flow. Additional information is contained in the original.

Joughin, Ian↗

NASA NTRS1998

Searching for Planets Around other Stars

In this colloquim presentation, Professor of Astronomy, Geoffrey Marcy discusses the discovery of planets orbiting other stars. Using the Doppler shift caused by stellar wobble that is caused by nearby planetary mass, astronomers have been able to infer the existence of Jupiter-sized planets around other stars. Using a special spectrometer at Lick Observatory, the wobble of several stars have been traced over the years required to generate an accurate pattern required to infer the stellar wobble. Professor Marcy, discusses the findings of planets around 47 Ursae Majoris, 16 Cygni B, 51 Pegasus, and 56 Rho 1 Cne. In the case of 56 Rho 1 Cne the planet appears to be close to the star, within 1.5 astronomical units. The observations from the smaller Lick Observatory will be augmented by new observations from the larger telescope at the Kek observatory. This move will allow observations of smaller planets, as opposed to the massive planets thus far discovered. The astronomers also hope to observe smaller stars with the Kek data. Future spaceborne observations will allow the discovery of even smaller planets. A spaceborne interferometer is in the planning stages, and an even larger observatory, called the Terrestrial Planet Finder, is hoped for. Professor Marcy shows artists' renderings of two of the planets thus far discovered. He also briefly discusses planetary formation and shows slides of both observations from the Orion Nebula and models of stellar system formation.

Source record↗

NASA NTRS2019

Using the NASA GeneLab Data System to Study the Metagenomes of Spaceships and Their Occupants

With humans pushing to live further off Earth for longer periods of time, it is increasingly important to understand the changes that occur in biological systems during spaceflight whether these be astronauts, their microbial commensals, or their plant-based life support systems. In a three-part presentation, we discuss GeneLab and recent discoveries regarding the microbiota of spacecrafts and space-flown animals. Part 1: GeneLab: Open Science for Life in Space, Jonathan Galazka, NASA Ames Research Center To accelerate the pace of discovery from precious spaceflight biological experiments, NASA as develop the GeneLab data system (genelab.nasa.gov), which allows unfettered access to omics data from spaceflight and spaceflight relevant experiments. GeneLab houses metagenomic datasets from spacecraft and relevant spacecraft models. Users can download this data and associated metadata to make new discoveries about how microbial communities may change and adapt to spaceflight.

Galazka, Jonathan M.↗

NASA NTRSDate not supplied

Discovering Research Areas in Dataset Applications Through Knowledge Graphs and Large Language Models

Scientific datasets are increasingly cited in peer-reviewed journal publications, facilitating easy access to research utilizing those datasets. Datasets undergo a life cycle where older versions of datasets are replaced by newer versions often due to improvements in data resolution, algorithms, and other factors. Unlike peer reviewed documents registered with a single Digital Unique Identifier (DOI), datasets can be updated over time and the newer version of the datasets are registered with a new DOI which is not necessarily linked to the previous version of the dataset. It is challenging when publications citing a dataset need to be traced over the entire life cycle of that dataset. We provide an innovative approach to link the dataset versions and publications using a knowledge graph (KG). KG can help to trace the dataset cited in publications over the entire dataset life cycle and shed light into dataset usage in various applied research areas. We fine-tuned the pretrained NASA IMPACTINDUS Large Language Model (LLM) on a set of labeled publications abstracts. Our results showed that 87% of the publications were classified into one of twenty applied research areas, while the remaining 13% were classified into non-applied research areas. By linking datasets to applied research areas through the KG and employing Global Change Master Directory(GCMD), a well-established controlled vocabulary of scientific keywords describing Earth science datasets, we contribute to a transparent and advanced search and discovery mechanism for datasets across the Earth data ecosystem. The integrated KG and LLM approach is now incorporated and operational in dataset publication management at one of NASA’s Earth science data archival centers.

data provenance↗

DOE OSTI2026

MCP-eGridGPT (MCP-Enabled Chatbot with Electrical Power System Analysis and Interactive Visualization Tool) [SWR-25-126]

This software is an advanced chatbot system that integrates the Model Context Protocol (MCP) to provide intelligent electrical power system analysis and automated visualization generation. The system enables users to interact with complex electrical engineering tools through natural language, automatically analyzes power system data for voltage violations and grid health assessment, and generates professional interactive HTML dashboards and reports. Key features include dynamic tool discovery from MCP servers, multi-LLM provider support, intelligent data interpretation using large language models, automated chart generation, and a web-based interface for real-time analysis. The software bridges sophisticated electrical engineering analysis with user-friendly interfaces, making power system diagnostics accessible through conversational AI.

Choi, Seong [National Laboratory of the Rockies (N↗

NASA NTRS2014

SGR J1745-29: Swift Discovery and Monitoring of a New SGR Near Sgr A*

Starting in 2013 February, Swift has been performing short daily monitoring observations of the G2 gas cloud near Sgr A* with the X-Ray Telescope to determine whether the cloud interaction leads to an increase in the flux from the Galactic center. On 2013 April 24 Swift detected an order of magnitude rise in the X-ray flux from the region near Sgr A*. Initially thought to be a flare from Sgr A*, detection of a short hard X-ray burst from the same region by the Burst Alert Telescope suggested that the flare was from an unresolved new Soft Gamma Repeater, SGR J1745-29. Here we present the discovery of SGR J1745-29 by Swift, including analysis of data before, during, and after the burst. We cover the entire light-curve of the SGR outburst so far, from discovery through to the source entering a Swift Sun constraint in November 2013. Thanks to the interest in G2 and its location near the Galactic Center, SGR J1745-29 has become one of the best monitoring SGRs in outburst yet seen.

Kennea, Jamie A.↗